1. Field of the Invention
The present invention relates to an adaptive image compression system and method, and more particularly, to an adaptive image compression system and method which can efficiently compress an image by analyzing the characteristics of the image to be compressed and determining a loss tolerance in which similar image quality can be maintained before compressing the image.
2. Description of Related Art
A variety of traditional image compression methods are well known in the art, in which joint photographic experts group (JPEG) compression is most popular and is widely used as the standard for compressing and expressing images.
Although the technical principle of the present invention can be applied to all types of data compression methods, an application thereof to the JPEG will be described as a representative example.
FIG. 1 is a block diagram schematically showing a method of generating a JPEG image of the related art.
Referring to FIG. 1, the JPEG is a method of compressing and storing original images. The JPEG method converts an original image into a frequency space data via discrete cosine transform (DCT) at S10, lossy-compresses the converted data by quantizing the converted data using a standard quantization table at S11, and generates a compressed image data by encoding the lossy-compressed data at S12.
When the lossy-compression such as JPEG is carried out, most loss in information occurs during the quantization. Therefore, in this method, the image compression ratio mainly depends on the scale at which the quantization is carried out.
However, it is preferred that an image be compressed within the range in which the visible image quality of a user does not greatly degrade. For example, although an increase in the image compression ratio (i.e. the increased quantization scale) has the effect of the decreased data size of the compressed image, this may significantly degrade the visual image quality, which is problematic. In addition, although a decrease in the image compression ratio does not significantly degrade the image quality, the degree to which the data is compressed is insignificant, which is problematic.
Therefore, a method of maximizing the compression ratio while maintaining visible image quality within the range of similar image quality (i.e. the range of image quality in which it is difficult to visibly discriminate visible image quality from original image quality), i.e. a method of optimizing the compression ratio with respect to the image quality, is being actively studied.
A related traditional method uses a scheme of searching for an optimization compression ratio by generally carrying out iterative compression (e.g. JPEG mini). FIG. 2 shows such an example of this method.
Referring to FIG. 2, the method of the related art includes step S20 of quantizing an image that is converted into a data in a frequency range at a predetermined quantization scale, i.e. adjusting DCT coefficients for respective blocks, and step S21 of determining whether or not image quality has degraded. When the image quality has not degraded, the method iterates the step S20 of adjusting DCT coefficients for respective blocks. In order to determine whether or not the image quality has degraded, it is possible to use a scheme of inspecting whether or not an artifact, i.e. an artificial image that causes discrepancies from similar image quality, has occurred. Of course, also in this case, the quantization can use a quantization table that corresponds to the quantization scale according to each step. The quantization scale according to each step is determined based on the value of the difference between the previous image and the compressed image. In addition, when the artifact is produced due to the iterative compression, at step S23, the compression can be completed by encoding information that has been compressed in the previous steps.
However, such a compression method of the related art may be inefficient since quantization is carried out without considering whether the image to be compressed is a complicated image, the visual image degradation of which is relatively small even though much of the information is lost, or is a simple image, the visual image degradation of which is relatively great even though less of information is lost. In addition, the compression speed is slow since the method iteratively determines whether or not to carry out the compression again or stop the compression once having carried out compression.
In addition, it is impossible to ensure that the final result of the compression be the optimum compression ratio, which is problematic. This is because, although the result of the previous quantization step becomes the final result of the compression when an artifact occurs as a result of the quantization step that is finally carried out, the result of the previous quantization step does not guarantee that the image is compressed at the optimization compression ratio.
Therefore, a method is required which non-iteratively compresses an image by determining a quantization level that guarantees visible image quality according to DCT coefficients depending on the characteristics of an image unlike the method of the related art. That is, a compression method is urgently required which determines a loss tolerance in which similar image quality is maintained by analyzing the characteristics of an image, determines in advance a quantization scale that is to be used for compression, and then generates an adaptive quantization table corresponding to the determined quantization scale. This method can consequently increase a compression ratio within the range of similar image quality by compressing the image once.